Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen attribute-object compositions in the training set. Previous works mainly project an image and its corresponding composition into a common embedding space to measure their compatibility score. However, both attributes and objects share the visual representations learned above, leading the model to exploit spurious correlations and bias towards seen compositions. Instead, we reconsider CZSL as an out-of-distribution generalization problem. If an object is treated as a domain, we can learn object-invariant features to recognize attributes attached to any object reliably, and vice versa. Specifically, we propose an invariant feature learning framework to align different domains at the representation and gradient levels to capture the intrinsic characteristics associated with the tasks. To further facilitate and encourage the disentanglement of attributes and objects, we propose an "encoding-reshuffling-decoding" process to help the model avoid spurious correlations by randomly regrouping the disentangled features into synthetic features. Ultimately, our method improves generalization by learning to disentangle features that represent two independent factors of attributes and objects. Experiments demonstrate that the proposed method achieves state-of-the-art or competitive performance in both closed-world and open-world scenarios.

Compositional zero-shot learning (CZSL) strives to learn attributes and objects from seen compositions and transfer the acquired knowledge to unseen compositions. Existing methods either learn primitive concepts in an entangled manner, leading to the model relying on spurious correlations between attributes and objects. Alternatively, they adopt a decoupled approach, causing the model to overlook relationships between attributes and objects. In this paper, we propose a conditional prompting (CoP) method to enhance the performance of vision-language models (e.g., CLIP) in CZSL. Specifically, we utilize two image-to-word mapping networks to learn pseudo attribute and object word embeddings that can represent the corresponding semantics of input images. Subsequently, the model recognizes one concept based on another generated pseudo word embeddings, enabling the recognition of individual sub-concepts while leveraging the image-specific correlations between attributes and objects. The experimental results on three CZSL benchmarks indicate that the proposed method achieves competitive performance compared to previous state-of-the-art methods.

Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.